Sudden, unforeseen machine breakdowns cause performance interruptions, supply bottlenecks and can lead to the shutdown of an entire process. By monitoring and analyzing real-time and historical machine data, changes can be detected early, reducing unplanned downtime to a minimum. Predictive maintenance is the maintenance strategy of the future, by means of which maintenance processes are initiated when they are highly likely to become necessary due to the evaluation of the machine data collected.
The pure monitoring of plants (condition monitoring) is enhanced by a statistical evaluation of the collected data and the prognosis of future malfunctions.
Insight into process and data streams
using leading data storage and visualization platforms.
Increased production reliability
through the continuous evaluation of real-time data.
is triggered and performed by accurate analysis and machine learning techniques.
Cost and time savings
through intelligent maintenance cycles and the avoidance of production downtimes.
Increase of reaction speed
by alerting functions.
Trust in our expertise!
We support you throughout the entire process.
We support you in the fields of
The basis for a successful predictive maintenance system is the generation and collection of data. Our team ensures that you get exactly the data from your machine that is necessary and useful for an optimised operation. Therefore we have experts in the areas of embedded programming and application development. In the area of hardware, we work together with renowned manufacturers such as ELTEC Elektronik AG or Wachendorff.
Storage of data
The enormous amounts of data collected must be stored in a central location, preferably in the form of a cloud environment. This requires in-depth knowledge of the common platforms AWS and Azure. We advise you on which cloud solution is best suited for your application and set it up together with you.
After an automated plausibility check and cleaning of the data, the data is ready for visualization. The visual preparation of the data allows a clear monitoring and analysis of real-time data. Changes in the condition of the machines can thus be detected even before the malfunction or failure. We support you in the development of dashboards and advanced data analysis using leading data storage and visualization platforms such as Siemens Mindsphere or Splunk.
Selection of storage and analysis platform
MindSphere offers a wide range of protocol options for device and enterprise applications, industry applications, extensive analytics and an innovative development environment that leverages both Siemens' open platform-as-a-service (PaaS) capabilities and access to AWS cloud services. Through these capabilities, MindSphere connects real things to the digital world and delivers powerful industry applications and digital services that drive business success.
MindSphere enables the development and deployment of new industry applications in a diverse partner ecosystem through open PaaS capabilities. Benefit from the experience and insights of our partners. In order to advance your IoT strategy, no development on your part is required.
Splunk provides a scalable, multi-platform for machine data that is generated by all the devices, control systems, sensors, SCADA systems, networks, applications and end users connected through modern networks.
Prognosis and Artificial Intelligence
With the help of statistical and self-learning methods, reliable forecasts can be derived from the collected data. These are used to draw the necessary conclusions regarding the maintenance of machines. Our mathematicians will advise you on the appropriate statistical methods for your application and support you in their implementation.